Human mobility, social ties, and link prediction

Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evo-lution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on indi-vidual movements and social interactions. Here we address this challenge for the first time by tracking the trajecto-ries and communication records of 6 Million mobile phone users. We find that the similarity between two individuals’ movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to ad-dress a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accu-racy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link pre-diction but also network dynamics